Stars
Simple static web-based mask drawer, supporting semantic segmentation and video segmentation with interactive Segment Anything Model 2 (SAM2).
State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server!
使用Vue实现类似https://segment-anything.com/ 中的效果
ISAT - Image segmentation annotation tool.（图像分割标注工具，支持语义分割与实例分割）
Labeling tool with SAM(segment anything model),supports SAM, SAM2, sam-hq, MobileSAM EdgeSAM etc.交互式半自动图像标注工具
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
高效交互式语义分割标注软件EISeg 『Efficient and intelligent interactive segmentation annotation software』
Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image …
电子科技大学 UESTC 信息与软件工程学院 信软 软件工程 数漫方向 数字动漫（icoding，编译技术，各科实验）
一个轻量级图片标注javascript库，支持矩形、多边形、点、折线、圆形，支持再编辑，使得图像标注更简单。
一款在线图像标注工具（矩形、多边形、持续更新中……），可用于深度学习实例分割模型训练（Mask R-CNN）等。
Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
3D Bounding Box Annotation Tool (3D-BAT) Point cloud and Image Labeling
An infinitely customizable image annotation library built on React
Source code for the LabelMe annotation tool.
半自动标注系统是基于BS架构，由鹏城实验室自主研发，集成视频抽帧，目标检测、视频跟踪、ReID分类、人脸检测等算法，实现了对图像，视频的自动标注，并可以对自动算法的结果进行人工标注，最终得到标注结果，同时也可以对视频、图片、医疗（包括dicom文件及病理图像）相关的数据进行人工标注，标注结果支持COCO及VOC格式。支持多人协同标注。 半自动标注系统主要功能有：用户管理，数据集管理，自动标注…
Character Simulator(Walk Simulator) is inspired by digital pavilions and third-person games, and is developed based on the "gallery". The technology stack uses Vue3+TypeScript+Vite
Digital exhibition project developed based on three.js.
Corruption and Perturbation Robustness (ICLR 2019)
总结了前端面试过程中浏览器，计算机网络，数据结构与算法，HTML，CSS，JS，Vue，React已经实战经验等相关的面试知识。
总结前端面试中经典的vue相关题目，分析最佳回答策略。加面试群关注公众号”村长学前端“。
Applying Transfer Learning on Fashion Product Images Dataset from Kaggle
标注自己的数据集，训练、评估、测试、部署自己的人工智能算法